This study proposes and experimentally validates a multifunctional, ultra-wideband polarization conversion metasurface. The design integrates polarization conversion and electromagnetic scattering functions into a single structure, enabling applications in polarization conversion, beam control, and effective reduction of the radar cross-section (RCS). The metasurface achieves linear-to-circular polarization conversion with an axial ratio (AR) of less than 3 dB across dual-band ranges of 14.6–26.8 GHz and 31–33.5 GHz. Additionally, by adjusting metallic resonant rings within the unit structure, cross-polarization conversion with a polarization conversion ratio (PCR) greater than 0.9 is realized in the 13.6–29.8 GHz frequency range, maintaining excellent stability even at oblique incidence angles up to 50°. Leveraging the phase cancellation principle, various coding arrays are designed to precisely control the scattered beams, reducing the RCS by more than 10 dB. The comparison of simulation and experimental results further validates the wide application potential of this polarization converter in fields such as wireless communication, antenna engineering, and radar stealth.

Radar based microwave imaging system is proposed and used for breast cancer detection. Tumour location can be detected based on high dielectric differences between tumour and surrounding tissues. In clinical medicine, malignant tumour usually has more irregular morphology than benign tumour. However, previous work has ignored the discrimination between benign and malignant tumours, and this issue is solved in our work. In this paper, two major novel contributions are presented. Firstly, a complex natural responses (CNRs) method is proposed for discrimination between the benign and malignant tumours. The CNRs of the tumour show a unique feature that the CNRs only depend on the morphological appearance of the tumour. Simulation results show that the use of the CNRs can successfully discriminate between a 5 mm radius spherical benign tumour and a 5 mm average radius malignant tumour with a spiculated periphery. Secondly, microwave images of the benign and malignant tumours are created by a proposed mono-static imaging system which uses a single Vivaldi antenna to scan the breast. Both benign and malignant tumour positions can be correctly indicated from the images.

Millimetre wave radar is an emerging technology that can monitor vital signs without contact. This unique feature is very suitable for some particular situations, such as burn patient monitoring. Currently, electrocardiogram (ECG) is still the most common approach for monitoring heart disease. Deep learning algorithms have already been applied to classifying ECG recordings and have achieved good diagnostic results. However, it is very rare to see deep learning-based heartbeat classification using radar signals. The reason is a lack of radar-based heart disease datasets, which are the most important part of training a Convolutional Neural Network (CNN). Specifically, the ECG recordings and radar signals are heterogeneous; thus, the ECG dataset cannot train the CNN for directly classifying the radar signals. In this paper, we propose a novel signal processing algorithm called the Common Features Extraction Method (CFEM) to extract the common features of ECG recordings and radar signals to train a CNN for radar heartbeat signal classification. By using CFEM, the ECG dataset is transferred to the radar field, which means that the core issue for training the CNN using radar signals has been solved. Practical experiments show that the CFEM-based CNN can classify heartbeat radar signals accurately.

In array signal processing, direction of arrival estimation (DOA estimation) is a common and extremely important method¹. This paper is based on the existing DOA algorithm, and conducts deeper research. Firstly, compare three DOA estimation algorithms using simulation program. The experimental results prove that the MUSIC algorithm has the highest accuracy. Secondly, we analysis the basic theory of MUSIC algorithm, and then discusses the factors of affecting the estimation accuracy of MUSIC algorithm. Finally, in view of the shortcomings of the MUSIC algorithm for solving the problem of coherent sources, this paper improves the MUSIC algorithm, then proposes an improved algorithm, which is verified by simulation experiments.

Likelihood probability based phylogenetic analysis approaches have contributed to impressive advances in minimizing the variance of estimating the evolutionary parameters. However, their actual applications are greatly limited due to the very time-consuming calculations of Conditional Likelihood Probabilities (CLPs). Accurately and quickly obtaining the likelihoods of massive tree samples can facilitate phylogenetic analysis process. Inspired by recent advance of machine learning techniques that greatly improve the performance of many related prediction tasks, this study proposes a Random Forest (RF) based learning and prediction approach, called NeoPLE. The approach initially learns the deep neighbor information between nodes from the topology representations of evolution trees, integrates likelihood information from these trees, and trains a non-linear prediction model. Instead of having to depend on the recursive calculations of the CLPs of tree nodes, NeoPLE transfers the process to a prediction by the trained model, thus the likelihood estimates become irrelevant with the calculations of CLPs. In terms of performance improvement, speedup factors ranging from 2.1 to 3.5X can be achieved on the analysis of realistic data sets. Moreover, NeoPLE is very suitable to handle the data sets having relatively large number of alignment sites, the factor of up to 27.5X can be achieved on the analysis of simulated data sets. In addition, NeoPLE is robust against a wide range of choices of evolutionary models and is ready to integrate in more phylogenetic inference tools. This study fills in the gaps of phylogenetic analysis using a machine learning approach in feature representation and likelihood prediction of evolution trees, which has not been reported in literatures.